We present the performance of a semantic segmentation network, sparsessnet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time ...projection chamber for the study of neutrino properties and interactions. sparsessnet is a submanifold sparse convolutional neural network, which provides the initial machine learning based algorithm utilized in one of MicroBooNEs νe-appearance oscillation analyses. The network is trained to categorize pixels into five classes, which are reclassified into two classes more relevant to the current analysis. The output of sparsessnet is a key input in further analysis steps. This technique, used for the first time in liquid argon time projection chambers data and is an improvement compared to a previously used convolutional neural network, both in accuracy and computing resource utilization. The accuracy achieved on the test sample is ≥ 99 %. For full neutrino interaction simulations, the time for processing one image is ≈ 0.5 sec , the memory usage is at 1 GB level, which allows utilization of most typical CPU worker machine.
We report on the first measurement of flux-integrated single differential cross sections for charged-current (CC) muon neutrino (νμ) scattering on argon with a muon and a proton in the final state, ...Ar40 (νμ,μp)X. The measurement was carried out using the Booster Neutrino Beam at Fermi National Accelerator Laboratory and the MicroBooNE liquid argon time projection chamber detector with an exposure of 4.59×1019 protons on target. Events are selected to enhance the contribution of CC quasielastic (CCQE) interactions. The data are reported in terms of a total cross section as well as single differential cross sections in final state muon and proton kinematics. We measure the integrated per-nucleus CCQE-like cross section (i.e., for interactions leading to a muon, one proton, and no pions above detection threshold) of (4.93±0.76stat±1.29sys)×10−38 cm2, in good agreement with theoretical calculations. The single differential cross sections are also in overall good agreement with theoretical predictions, except at very forward muon scattering angles that correspond to low-momentum-transfer events.
We present an analysis of MicroBooNE data with a signature of one muon, no pions, and at least one proton above a momentum threshold of 300 MeV/c(CC0πNp). This is the first differential cross-section ...measurement of this topology in neutrino-argon interactions. We achieve a significantly lower proton momentum threshold than previous carbon and scintillator-based experiments. Using data collected from a total of approximately 1.6 × 1020 protons on target, we measure the muon neutrino cross section for the CC0πNp interaction channel in argon at MicroBooNE in the Booster Neutrino Beam which has a mean energy of around 800 MeV. We present the results from a data sample with estimated efficiency of 29% and purity of 76% as differential cross sections in five reconstructed variables: the muon momentum and polar angle, the leading proton momentum and polar angle, and the muon-proton opening angle. We include smearing matrices that can be used to "forward fold" theoretical predictions for comparison with these data. We compare the measured differential cross sections to a number of recent theory predictions demonstrating largely good agreement with this first-ever dataset on argon.
We present a search for the decays of a neutral scalar boson produced by kaons decaying at rest, in the context of the Higgs portal model, using the MicroBooNE detector. We analyze data triggered in ...time with the Fermilab NuMI neutrino beam spill, with an exposure of 1020 protons on target. We look for monoenergetic scalars that come from the direction of the NuMI hadron absorber, at a distance of 100 m from the detector, and decay to electron-positron pairs. We observe one candidate event, with a standard model background prediction of 1.9±0.8. We set an upper limit on the scalar–Higgs mixing angle of θ<(3.3−4.6)×10−4 at the 95% confidence level for scalar boson masses in the range(100–200) MeV/c2. We exclude, at the 95% confidence level, the remaining model parameters required to explain the central value of a possible excess of KL0→π0νν¯ decays reported by the KOTO collaboration. We also provide a model-independent limit on a new boson X produced in K→πX decays and decaying to e+e−.
We report results from a search for neutrino-induced neutral current (NC) resonant Δ(1232) baryon production followed by Δ radiative decay, with a ⟨0.8⟩ GeV neutrino beam. Data corresponding to ...MicroBooNE's first three years of operations (6.80×10^{20} protons on target) are used to select single-photon events with one or zero protons and without charged leptons in the final state (1γ1p and 1γ0p, respectively). The background is constrained via an in situ high-purity measurement of NC π^{0} events, made possible via dedicated 2γ1p and 2γ0p selections. A total of 16 and 153 events are observed for the 1γ1p and 1γ0p selections, respectively, compared to a constrained background prediction of 20.5±3.65(syst) and 145.1±13.8(syst) events. The data lead to a bound on an anomalous enhancement of the normalization of NC Δ radiative decay of less than 2.3 times the predicted nominal rate for this process at the 90% confidence level (C.L.). The measurement disfavors a candidate photon interpretation of the MiniBooNE low-energy excess as a factor of 3.18 times the nominal NC Δ radiative decay rate at the 94.8% C.L., in favor of the nominal prediction, and represents a greater than 50-fold improvement over the world's best limit on single-photon production in NC interactions in the sub-GeV neutrino energy range.
We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an ...interaction includes an e−, γ , μ−, π±, and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017). MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep-learning-based ν e search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.
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